In 2019, Cabrera et. al. presented FairVis [1]}, which is a rich visualization tool, where fairness metrics can be investigated for different intersectional subgroups.
FairVis additionally integrates methods for suggesting and discovering subgroups to investigate.
In the case of large label spaces, however, their proposed subgroup analysis is not applicable, as the attributes needed for such a subgroup analysis are often missing, for example when operating on unlabeled or sparsely labeled data.
Additionally, the visualizations that FairVis introduces are not designed for the large number of annotations that large label spaces contain.
